Evaluation of normalization methods for RNA-Seq gene expression estimation
Statistical inferences on RNA-Seq data, e.g., detecting differential gene expression, are meaningful only after proper normalization. However, there is no consensus for choosing a normalization procedure from among the many existing procedures. We evaluated several RNA-Seq normalization procedures b...
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Published in | 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Vol. 2011; pp. 50 - 57 |
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Main Authors | , , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
United States
IEEE
01.11.2011
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Subjects | |
Online Access | Get full text |
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Summary: | Statistical inferences on RNA-Seq data, e.g., detecting differential gene expression, are meaningful only after proper normalization. However, there is no consensus for choosing a normalization procedure from among the many existing procedures. We evaluated several RNA-Seq normalization procedures by (1) correlating estimated RNA-Seq expression values to those of microarrays, (2) examining the concordance of stable and differential gene detection between the platforms, and (3) applying the procedures to simulated RNA-Seq data. Results suggested that RNA-Seq normalization procedures have little effect on both inter-platform gene expression correlation as well as inter-platform concordance of genes detected as stably or differentially expressed. However, the results of simulated analysis suggested that some normalization procedures are more robust to changes in distribution of differentially expressed genes. These results may provide guidance for selecting RNA-Seq normalization procedures. |
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ISBN: | 9781457716126 1457716127 |
DOI: | 10.1109/BIBMW.2011.6112354 |